Overview

Dataset statistics

Number of variables21
Number of observations65241
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 MiB
Average record size in memory168.0 B

Variable types

Numeric14
Categorical7

Alerts

DateKey has a high cardinality: 558 distinct valuesHigh cardinality
Invoice Date has a high cardinality: 558 distinct valuesHigh cardinality
Item Number has a high cardinality: 983 distinct valuesHigh cardinality
Item has a high cardinality: 650 distinct valuesHigh cardinality
Promised Delivery Date has a high cardinality: 590 distinct valuesHigh cardinality
Discount Amount is highly overall correlated with Sales Amount and 3 other fieldsHigh correlation
List Price is highly overall correlated with Sales Price and 1 other fieldsHigh correlation
Sales Amount is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Amount Based on List Price is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Cost Amount is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Margin Amount is highly overall correlated with Discount Amount and 3 other fieldsHigh correlation
Sales Price is highly overall correlated with List Price and 1 other fieldsHigh correlation
Sales Quantity is highly overall correlated with List Price and 1 other fieldsHigh correlation
Item Class is highly imbalanced (99.7%)Imbalance
U/M is highly imbalanced (68.2%)Imbalance
Sales Cost Amount is highly skewed (γ1 = 21.0046152)Skewed
Sales Quantity is highly skewed (γ1 = 23.00067352)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Discount Amount has 1214 (1.9%) zerosZeros

Reproduction

Analysis started2023-01-21 08:05:36.744951
Analysis finished2023-01-21 08:06:35.162081
Duration58.42 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct65241
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32649.053
Minimum1
Maximum65281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:35.511539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3276
Q116324
median32654
Q348970
95-th percentile62019
Maximum65281
Range65280
Interquartile range (IQR)32646

Descriptive statistics

Standard deviation18844.646
Coefficient of variation (CV)0.57718813
Kurtosis-1.2005117
Mean32649.053
Median Absolute Deviation (MAD)16323
Skewness-0.00023741259
Sum2.1300569 × 109
Variance3.5512067 × 108
MonotonicityStrictly increasing
2023-01-21T13:36:35.874314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
43539 1
 
< 0.1%
43526 1
 
< 0.1%
43527 1
 
< 0.1%
43528 1
 
< 0.1%
43529 1
 
< 0.1%
43530 1
 
< 0.1%
43531 1
 
< 0.1%
43532 1
 
< 0.1%
43533 1
 
< 0.1%
Other values (65231) 65231
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
ValueCountFrequency (%)
65281 1
< 0.1%
65280 1
< 0.1%
65279 1
< 0.1%
65278 1
< 0.1%
65277 1
< 0.1%
65276 1
< 0.1%
65275 1
< 0.1%
65274 1
< 0.1%
65273 1
< 0.1%
65272 1
< 0.1%

CustKey
Real number (ℝ)

Distinct615
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10017703
Minimum10000453
Maximum10027583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:36.147192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10000453
5-th percentile10002506
Q110012715
median10019665
Q310023511
95-th percentile10026006
Maximum10027583
Range27130
Interquartile range (IQR)10796

Descriptive statistics

Standard deviation7175.8459
Coefficient of variation (CV)0.00071631648
Kurtosis-0.37098298
Mean10017703
Median Absolute Deviation (MAD)4886
Skewness-0.77030273
Sum6.5356497 × 1011
Variance51492764
MonotonicityNot monotonic
2023-01-21T13:36:36.343214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10025919 2760
 
4.2%
10019194 2752
 
4.2%
10012715 1431
 
2.2%
10012226 1389
 
2.1%
10025025 1143
 
1.8%
10023524 1042
 
1.6%
10020515 1010
 
1.5%
10017638 790
 
1.2%
10022456 741
 
1.1%
10002506 714
 
1.1%
Other values (605) 51469
78.9%
ValueCountFrequency (%)
10000453 329
0.5%
10000455 19
 
< 0.1%
10000456 104
 
0.2%
10000457 19
 
< 0.1%
10000458 10
 
< 0.1%
10000460 120
 
0.2%
10000461 251
0.4%
10000462 3
 
< 0.1%
10000466 123
 
0.2%
10000469 162
0.2%
ValueCountFrequency (%)
10027583 25
 
< 0.1%
10027575 5
 
< 0.1%
10027572 52
 
0.1%
10027560 42
 
0.1%
10027381 108
0.2%
10027370 234
0.4%
10027356 21
 
< 0.1%
10027348 14
 
< 0.1%
10027340 35
 
0.1%
10027119 175
0.3%

DateKey
Categorical

Distinct558
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size509.8 KiB
23/06/2017
 
460
05/07/2019
 
447
27/06/2017
 
313
18/11/2017
 
313
11/01/2017
 
310
Other values (553)
63398 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters652410
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14/07/2017
2nd row17/10/2017
3rd row27/05/2017
4th row03/09/2017
5th row18/06/2017

Common Values

ValueCountFrequency (%)
23/06/2017 460
 
0.7%
05/07/2019 447
 
0.7%
27/06/2017 313
 
0.5%
18/11/2017 313
 
0.5%
11/01/2017 310
 
0.5%
09/07/2019 307
 
0.5%
30/11/2019 307
 
0.5%
26/06/2017 294
 
0.5%
08/07/2019 279
 
0.4%
29/04/2017 279
 
0.4%
Other values (548) 61932
94.9%

Length

2023-01-21T13:36:36.646411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23/06/2017 460
 
0.7%
05/07/2019 447
 
0.7%
27/06/2017 313
 
0.5%
18/11/2017 313
 
0.5%
11/01/2017 310
 
0.5%
09/07/2019 307
 
0.5%
30/11/2019 307
 
0.5%
26/06/2017 294
 
0.5%
29/04/2017 279
 
0.4%
08/07/2019 279
 
0.4%
Other values (548) 61932
94.9%

Most occurring characters

ValueCountFrequency (%)
0 144497
22.1%
/ 130482
20.0%
1 122034
18.7%
2 105577
16.2%
7 41471
 
6.4%
9 40766
 
6.2%
8 18237
 
2.8%
3 16726
 
2.6%
6 11652
 
1.8%
5 11348
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 521928
80.0%
Other Punctuation 130482
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 144497
27.7%
1 122034
23.4%
2 105577
20.2%
7 41471
 
7.9%
9 40766
 
7.8%
8 18237
 
3.5%
3 16726
 
3.2%
6 11652
 
2.2%
5 11348
 
2.2%
4 9620
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/ 130482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 652410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 144497
22.1%
/ 130482
20.0%
1 122034
18.7%
2 105577
16.2%
7 41471
 
6.4%
9 40766
 
6.2%
8 18237
 
2.8%
3 16726
 
2.6%
6 11652
 
1.8%
5 11348
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 652410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 144497
22.1%
/ 130482
20.0%
1 122034
18.7%
2 105577
16.2%
7 41471
 
6.4%
9 40766
 
6.2%
8 18237
 
2.8%
3 16726
 
2.6%
6 11652
 
1.8%
5 11348
 
1.7%

Discount Amount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17730
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1857.3109
Minimum-255820.8
Maximum343532.66
Zeros1214
Zeros (%)1.9%
Negative933
Negative (%)1.4%
Memory size509.8 KiB
2023-01-21T13:36:36.902683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-255820.8
5-th percentile19.0422
Q1246.28
median442.2
Q31001.5
95-th percentile6354.225
Maximum343532.66
Range599353.46
Interquartile range (IQR)755.22

Descriptive statistics

Standard deviation9039.5358
Coefficient of variation (CV)4.8670019
Kurtosis379.55115
Mean1857.3109
Median Absolute Deviation (MAD)234.14
Skewness10.83909
Sum1.2117282 × 108
Variance81713207
MonotonicityNot monotonic
2023-01-21T13:36:37.138686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1214
 
1.9%
24.88 103
 
0.2%
606.84 100
 
0.2%
639.82 97
 
0.1%
601.9033 93
 
0.1%
634.6133 93
 
0.1%
402.7 93
 
0.1%
918.1412 88
 
0.1%
169.36 87
 
0.1%
385.98 87
 
0.1%
Other values (17720) 63186
96.9%
ValueCountFrequency (%)
-255820.8 1
 
< 0.1%
-245587.97 1
 
< 0.1%
-238792.73 1
 
< 0.1%
-231837.6 3
< 0.1%
-222564.1 3
< 0.1%
-127176 1
 
< 0.1%
-122088.96 1
 
< 0.1%
-84573.72 1
 
< 0.1%
-81190.77 1
 
< 0.1%
-53626 1
 
< 0.1%
ValueCountFrequency (%)
343532.66 2
< 0.1%
339103.35 1
 
< 0.1%
331487.76 2
< 0.1%
327213.75 1
 
< 0.1%
322454.09 1
 
< 0.1%
210371 4
< 0.1%
202995 4
< 0.1%
191196.5532 2
< 0.1%
189333.9 1
 
< 0.1%
182832.8832 2
< 0.1%

Invoice Date
Categorical

Distinct558
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size509.8 KiB
23/06/2017
 
460
05/07/2019
 
447
27/06/2017
 
313
18/11/2017
 
313
11/01/2017
 
310
Other values (553)
63398 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters652410
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14/07/2017
2nd row17/10/2017
3rd row27/05/2017
4th row03/09/2017
5th row18/06/2017

Common Values

ValueCountFrequency (%)
23/06/2017 460
 
0.7%
05/07/2019 447
 
0.7%
27/06/2017 313
 
0.5%
18/11/2017 313
 
0.5%
11/01/2017 310
 
0.5%
09/07/2019 307
 
0.5%
30/11/2019 307
 
0.5%
26/06/2017 294
 
0.5%
08/07/2019 279
 
0.4%
29/04/2017 279
 
0.4%
Other values (548) 61932
94.9%

Length

2023-01-21T13:36:37.423021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23/06/2017 460
 
0.7%
05/07/2019 447
 
0.7%
27/06/2017 313
 
0.5%
18/11/2017 313
 
0.5%
11/01/2017 310
 
0.5%
09/07/2019 307
 
0.5%
30/11/2019 307
 
0.5%
26/06/2017 294
 
0.5%
29/04/2017 279
 
0.4%
08/07/2019 279
 
0.4%
Other values (548) 61932
94.9%

Most occurring characters

ValueCountFrequency (%)
0 144497
22.1%
/ 130482
20.0%
1 122034
18.7%
2 105577
16.2%
7 41471
 
6.4%
9 40766
 
6.2%
8 18237
 
2.8%
3 16726
 
2.6%
6 11652
 
1.8%
5 11348
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 521928
80.0%
Other Punctuation 130482
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 144497
27.7%
1 122034
23.4%
2 105577
20.2%
7 41471
 
7.9%
9 40766
 
7.8%
8 18237
 
3.5%
3 16726
 
3.2%
6 11652
 
2.2%
5 11348
 
2.2%
4 9620
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/ 130482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 652410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 144497
22.1%
/ 130482
20.0%
1 122034
18.7%
2 105577
16.2%
7 41471
 
6.4%
9 40766
 
6.2%
8 18237
 
2.8%
3 16726
 
2.6%
6 11652
 
1.8%
5 11348
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 652410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 144497
22.1%
/ 130482
20.0%
1 122034
18.7%
2 105577
16.2%
7 41471
 
6.4%
9 40766
 
6.2%
8 18237
 
2.8%
3 16726
 
2.6%
6 11652
 
1.8%
5 11348
 
1.7%

Invoice Number
Real number (ℝ)

Distinct24650
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216292.79
Minimum100034
Maximum332842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:37.587169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum100034
5-th percentile104168
Q1117969
median222904
Q3314325
95-th percentile328904
Maximum332842
Range232808
Interquartile range (IQR)196356

Descriptive statistics

Standard deviation94982.019
Coefficient of variation (CV)0.43913632
Kurtosis-1.8436257
Mean216292.79
Median Absolute Deviation (MAD)98209
Skewness0.00055331482
Sum1.4111158 × 1010
Variance9.0215839 × 109
MonotonicityNot monotonic
2023-01-21T13:36:37.747231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225396 103
 
0.2%
113089 101
 
0.2%
125396 101
 
0.2%
126657 101
 
0.2%
313089 100
 
0.2%
325396 100
 
0.2%
326657 100
 
0.2%
129620 97
 
0.1%
116099 97
 
0.1%
316099 94
 
0.1%
Other values (24640) 64247
98.5%
ValueCountFrequency (%)
100034 1
< 0.1%
100080 1
< 0.1%
100093 1
< 0.1%
100094 1
< 0.1%
100130 1
< 0.1%
100132 1
< 0.1%
100204 1
< 0.1%
100222 1
< 0.1%
100229 1
< 0.1%
100230 1
< 0.1%
ValueCountFrequency (%)
332842 1
 
< 0.1%
332840 3
 
< 0.1%
332837 14
< 0.1%
332831 3
 
< 0.1%
332828 3
 
< 0.1%
332826 1
 
< 0.1%
332823 3
 
< 0.1%
332821 6
< 0.1%
332820 1
 
< 0.1%
332818 1
 
< 0.1%

Item Class
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size509.8 KiB
P01
65225 
PO1
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters195723
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP01
2nd rowP01
3rd rowP01
4th rowP01
5th rowP01

Common Values

ValueCountFrequency (%)
P01 65225
> 99.9%
PO1 16
 
< 0.1%

Length

2023-01-21T13:36:37.908249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T13:36:38.175159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
p01 65225
> 99.9%
po1 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
P 65241
33.3%
1 65241
33.3%
0 65225
33.3%
O 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130466
66.7%
Uppercase Letter 65257
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 65241
> 99.9%
O 16
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 65241
50.0%
0 65225
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130466
66.7%
Latin 65257
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 65241
> 99.9%
O 16
 
< 0.1%
Common
ValueCountFrequency (%)
1 65241
50.0%
0 65225
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 65241
33.3%
1 65241
33.3%
0 65225
33.3%
O 16
 
< 0.1%

Item Number
Categorical

Distinct983
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size509.8 KiB
17801
 
1610
28401
 
1344
27550
 
1182
67550
 
1126
20910
 
1050
Other values (978)
58929 

Length

Max length25
Median length5
Mean length5.0427185
Min length1

Characters and Unicode

Total characters328992
Distinct characters38
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.1%

Sample

1st row20910
2nd row38076
3rd row60776
4th row20910
5th row62550

Common Values

ValueCountFrequency (%)
17801 1610
 
2.5%
28401 1344
 
2.1%
27550 1182
 
1.8%
67550 1126
 
1.7%
20910 1050
 
1.6%
11690 1045
 
1.6%
47801 1043
 
1.6%
36001 947
 
1.5%
38076 942
 
1.4%
26502 913
 
1.4%
Other values (973) 54039
82.8%

Length

2023-01-21T13:36:38.385268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17801 1610
 
2.5%
28401 1344
 
2.1%
27550 1182
 
1.8%
67550 1126
 
1.7%
20910 1050
 
1.6%
11690 1045
 
1.6%
47801 1043
 
1.6%
36001 947
 
1.5%
38076 942
 
1.4%
26502 913
 
1.4%
Other values (982) 54100
82.8%

Most occurring characters

ValueCountFrequency (%)
0 53332
16.2%
6 40050
12.2%
2 38591
11.7%
3 38533
11.7%
1 31241
9.5%
5 28577
8.7%
8 26759
8.1%
7 25335
7.7%
9 24277
7.4%
4 21437
6.5%
Other values (28) 860
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 328132
99.7%
Uppercase Letter 735
 
0.2%
Space Separator 64
 
< 0.1%
Dash Punctuation 26
 
< 0.1%
Other Punctuation 25
 
< 0.1%
Lowercase Letter 7
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 259
35.2%
A 87
 
11.8%
R 54
 
7.3%
S 47
 
6.4%
E 37
 
5.0%
I 36
 
4.9%
Z 35
 
4.8%
N 34
 
4.6%
D 33
 
4.5%
T 28
 
3.8%
Other values (11) 85
 
11.6%
Decimal Number
ValueCountFrequency (%)
0 53332
16.3%
6 40050
12.2%
2 38591
11.8%
3 38533
11.7%
1 31241
9.5%
5 28577
8.7%
8 26759
8.2%
7 25335
7.7%
9 24277
7.4%
4 21437
6.5%
Other Punctuation
ValueCountFrequency (%)
/ 23
92.0%
# 2
 
8.0%
Space Separator
ValueCountFrequency (%)
64
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Lowercase Letter
ValueCountFrequency (%)
z 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 328250
99.8%
Latin 742
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 259
34.9%
A 87
 
11.7%
R 54
 
7.3%
S 47
 
6.3%
E 37
 
5.0%
I 36
 
4.9%
Z 35
 
4.7%
N 34
 
4.6%
D 33
 
4.4%
T 28
 
3.8%
Other values (12) 92
 
12.4%
Common
ValueCountFrequency (%)
0 53332
16.2%
6 40050
12.2%
2 38591
11.8%
3 38533
11.7%
1 31241
9.5%
5 28577
8.7%
8 26759
8.2%
7 25335
7.7%
9 24277
7.4%
4 21437
6.5%
Other values (6) 118
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 328992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53332
16.2%
6 40050
12.2%
2 38591
11.7%
3 38533
11.7%
1 31241
9.5%
5 28577
8.7%
8 26759
8.1%
7 25335
7.7%
9 24277
7.4%
4 21437
6.5%
Other values (28) 860
 
0.3%

Item
Categorical

Distinct650
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size509.8 KiB
Better Fancy Canned Sardines
 
1648
Ebony Prepared Salad
 
1471
Moms Sliced Turkey
 
1192
Imagine Popsicles
 
1191
Discover Manicotti
 
1126
Other values (645)
58613 

Length

Max length37
Median length32
Mean length21.722582
Min length8

Characters and Unicode

Total characters1417203
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowMoms Sliced Turkey
2nd rowCutting Edge Foot-Long Hot Dogs
3rd rowHigh Top Sweet Onion
4th rowMoms Sliced Turkey
5th rowTell Tale Garlic

Common Values

ValueCountFrequency (%)
Better Fancy Canned Sardines 1648
 
2.5%
Ebony Prepared Salad 1471
 
2.3%
Moms Sliced Turkey 1192
 
1.8%
Imagine Popsicles 1191
 
1.8%
Discover Manicotti 1126
 
1.7%
Red Spade Foot-Long Hot Dogs 1075
 
1.6%
High Top Dried Mushrooms 1072
 
1.6%
Big Time Frozen Cheese Pizza 947
 
1.5%
Cutting Edge Foot-Long Hot Dogs 942
 
1.4%
Bravo Large Canned Shrimp 941
 
1.4%
Other values (640) 53636
82.2%

Length

2023-01-21T13:36:38.605430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canned 6375
 
2.8%
ebony 5460
 
2.4%
cheese 5192
 
2.3%
better 4569
 
2.0%
red 4271
 
1.9%
top 4172
 
1.8%
spade 4161
 
1.8%
high 4137
 
1.8%
best 3477
 
1.5%
nationeel 3328
 
1.4%
Other values (294) 184407
80.3%

Most occurring characters

ValueCountFrequency (%)
164308
 
11.6%
e 146997
 
10.4%
o 92424
 
6.5%
a 92229
 
6.5%
n 74029
 
5.2%
i 69406
 
4.9%
t 68697
 
4.8%
r 67309
 
4.7%
l 59799
 
4.2%
s 57763
 
4.1%
Other values (46) 524242
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1013133
71.5%
Uppercase Letter 236106
 
16.7%
Space Separator 164308
 
11.6%
Dash Punctuation 2160
 
0.2%
Other Punctuation 748
 
0.1%
Decimal Number 748
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 146997
14.5%
o 92424
 
9.1%
a 92229
 
9.1%
n 74029
 
7.3%
i 69406
 
6.9%
t 68697
 
6.8%
r 67309
 
6.6%
l 59799
 
5.9%
s 57763
 
5.7%
d 40526
 
4.0%
Other values (16) 243954
24.1%
Uppercase Letter
ValueCountFrequency (%)
B 31413
13.3%
C 30848
13.1%
S 26119
11.1%
T 19360
 
8.2%
F 16230
 
6.9%
M 12512
 
5.3%
P 11463
 
4.9%
L 11018
 
4.7%
D 10750
 
4.6%
E 10232
 
4.3%
Other values (15) 56161
23.8%
Decimal Number
ValueCountFrequency (%)
1 579
77.4%
2 169
 
22.6%
Space Separator
ValueCountFrequency (%)
164308
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2160
100.0%
Other Punctuation
ValueCountFrequency (%)
% 748
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1249239
88.1%
Common 167964
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 146997
 
11.8%
o 92424
 
7.4%
a 92229
 
7.4%
n 74029
 
5.9%
i 69406
 
5.6%
t 68697
 
5.5%
r 67309
 
5.4%
l 59799
 
4.8%
s 57763
 
4.6%
d 40526
 
3.2%
Other values (41) 480060
38.4%
Common
ValueCountFrequency (%)
164308
97.8%
- 2160
 
1.3%
% 748
 
0.4%
1 579
 
0.3%
2 169
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1417203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
164308
 
11.6%
e 146997
 
10.4%
o 92424
 
6.5%
a 92229
 
6.5%
n 74029
 
5.2%
i 69406
 
4.9%
t 68697
 
4.8%
r 67309
 
4.7%
l 59799
 
4.2%
s 57763
 
4.1%
Other values (46) 524242
37.0%

Line Number
Real number (ℝ)

Distinct397
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23725.043
Minimum1000
Maximum344000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:38.940991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q13000
median12000
Q332000
95-th percentile88000
Maximum344000
Range343000
Interquartile range (IQR)29000

Descriptive statistics

Standard deviation32669.565
Coefficient of variation (CV)1.3770076
Kurtosis11.146323
Mean23725.043
Median Absolute Deviation (MAD)11000
Skewness2.8814917
Sum1.5478455 × 109
Variance1.0673005 × 109
MonotonicityNot monotonic
2023-01-21T13:36:39.419209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 11189
 
17.2%
2000 3696
 
5.7%
3000 2763
 
4.2%
4000 2287
 
3.5%
5000 1947
 
3.0%
6000 1839
 
2.8%
9000 1614
 
2.5%
7000 1586
 
2.4%
8000 1464
 
2.2%
10000 1412
 
2.2%
Other values (387) 35444
54.3%
ValueCountFrequency (%)
1000 11189
17.2%
1001 247
 
0.4%
1002 46
 
0.1%
1003 5
 
< 0.1%
1100 4
 
< 0.1%
1101 4
 
< 0.1%
2000 3696
 
5.7%
2001 158
 
0.2%
2002 36
 
0.1%
3000 2763
 
4.2%
ValueCountFrequency (%)
344000 2
 
< 0.1%
330000 2
 
< 0.1%
320000 2
 
< 0.1%
314001 2
 
< 0.1%
268000 2
 
< 0.1%
260000 2
 
< 0.1%
249000 2
 
< 0.1%
246000 2
 
< 0.1%
244000 2
 
< 0.1%
243000 7
< 0.1%

List Price
Real number (ℝ)

Distinct1062
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean515.01683
Minimum0
Maximum2760.7
Zeros255
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:39.687379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36.69
Q1181.56
median325.19
Q3803.86
95-th percentile1431.23
Maximum2760.7
Range2760.7
Interquartile range (IQR)622.3

Descriptive statistics

Standard deviation449.1449
Coefficient of variation (CV)0.8720975
Kurtosis0.011618476
Mean515.01683
Median Absolute Deviation (MAD)217.35
Skewness1.0051813
Sum33600213
Variance201731.14
MonotonicityNot monotonic
2023-01-21T13:36:39.982077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298 1508
 
2.3%
1431.23 1426
 
2.2%
966.44 1192
 
1.8%
1275.1 1126
 
1.7%
192.34 1041
 
1.6%
1627.84 1035
 
1.6%
157.76 988
 
1.5%
1084.61 975
 
1.5%
181.44 893
 
1.4%
412.03 892
 
1.4%
Other values (1052) 54165
83.0%
ValueCountFrequency (%)
0 255
0.4%
0.3929 150
0.2%
0.4 21
 
< 0.1%
0.405 25
 
< 0.1%
0.41 10
 
< 0.1%
0.445 6
 
< 0.1%
0.52 1
 
< 0.1%
0.61 4
 
< 0.1%
1.6236 2
 
< 0.1%
1.8711 9
 
< 0.1%
ValueCountFrequency (%)
2760.7 12
 
< 0.1%
2291.4 7
 
< 0.1%
2267 10
 
< 0.1%
1975 113
0.2%
1920 61
0.1%
1880 19
 
< 0.1%
1759.4 45
 
0.1%
1731.4 35
 
0.1%
1691.4 12
 
< 0.1%
1688.13 150
0.2%

Order Number
Real number (ℝ)

Distinct17769
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180567.61
Minimum100838
Maximum321532
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:40.297554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum100838
5-th percentile103465
Q1115281
median203695
Q3218576
95-th percentile319116
Maximum321532
Range220694
Interquartile range (IQR)103295

Descriptive statistics

Standard deviation67612.239
Coefficient of variation (CV)0.37444278
Kurtosis-0.63225366
Mean180567.61
Median Absolute Deviation (MAD)80306
Skewness0.52670753
Sum1.1780411 × 1010
Variance4.5714148 × 109
MonotonicityNot monotonic
2023-01-21T13:36:40.499133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210770 126
 
0.2%
222244 126
 
0.2%
110770 124
 
0.2%
122244 124
 
0.2%
205051 121
 
0.2%
105051 118
 
0.2%
320895 118
 
0.2%
212870 117
 
0.2%
220895 116
 
0.2%
120895 115
 
0.2%
Other values (17759) 64036
98.2%
ValueCountFrequency (%)
100838 1
 
< 0.1%
100879 1
 
< 0.1%
100880 1
 
< 0.1%
100881 1
 
< 0.1%
100882 3
< 0.1%
100883 3
< 0.1%
100884 3
< 0.1%
100888 1
 
< 0.1%
100889 1
 
< 0.1%
100890 1
 
< 0.1%
ValueCountFrequency (%)
321532 5
 
< 0.1%
321528 11
< 0.1%
321519 1
 
< 0.1%
321509 3
 
< 0.1%
321504 1
 
< 0.1%
321500 1
 
< 0.1%
321496 1
 
< 0.1%
321489 1
 
< 0.1%
321483 15
< 0.1%
321482 1
 
< 0.1%
Distinct590
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size509.8 KiB
27/06/2017
 
309
11/01/2017
 
305
09/07/2019
 
303
17/11/2017
 
273
29/11/2019
 
264
Other values (585)
63787 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters652410
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st row14/07/2017
2nd row16/10/2017
3rd row28/05/2017
4th row03/09/2017
5th row18/06/2017

Common Values

ValueCountFrequency (%)
27/06/2017 309
 
0.5%
11/01/2017 305
 
0.5%
09/07/2019 303
 
0.5%
17/11/2017 273
 
0.4%
29/11/2019 264
 
0.4%
18/06/2017 248
 
0.4%
21/06/2017 238
 
0.4%
30/06/2019 235
 
0.4%
03/07/2019 231
 
0.4%
10/01/2017 231
 
0.4%
Other values (580) 62604
96.0%

Length

2023-01-21T13:36:40.696937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27/06/2017 309
 
0.5%
11/01/2017 305
 
0.5%
09/07/2019 303
 
0.5%
17/11/2017 273
 
0.4%
29/11/2019 264
 
0.4%
18/06/2017 248
 
0.4%
21/06/2017 238
 
0.4%
30/06/2019 235
 
0.4%
03/07/2019 231
 
0.4%
10/01/2017 231
 
0.4%
Other values (580) 62604
96.0%

Most occurring characters

ValueCountFrequency (%)
0 144423
22.1%
/ 130482
20.0%
1 122077
18.7%
2 105615
16.2%
7 41965
 
6.4%
9 40897
 
6.3%
8 18432
 
2.8%
3 16342
 
2.5%
6 11308
 
1.7%
5 11164
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 521928
80.0%
Other Punctuation 130482
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 144423
27.7%
1 122077
23.4%
2 105615
20.2%
7 41965
 
8.0%
9 40897
 
7.8%
8 18432
 
3.5%
3 16342
 
3.1%
6 11308
 
2.2%
5 11164
 
2.1%
4 9705
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/ 130482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 652410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 144423
22.1%
/ 130482
20.0%
1 122077
18.7%
2 105615
16.2%
7 41965
 
6.4%
9 40897
 
6.3%
8 18432
 
2.8%
3 16342
 
2.5%
6 11308
 
1.7%
5 11164
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 652410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 144423
22.1%
/ 130482
20.0%
1 122077
18.7%
2 105615
16.2%
7 41965
 
6.4%
9 40897
 
6.3%
8 18432
 
2.8%
3 16342
 
2.5%
6 11308
 
1.7%
5 11164
 
1.7%

Sales Amount
Real number (ℝ)

Distinct17866
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2853.1211
Minimum200.01
Maximum555376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:40.938323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum200.01
5-th percentile215.78
Q1308.31
median553.94
Q31279.75
95-th percentile8795.9
Maximum555376
Range555175.99
Interquartile range (IQR)971.44

Descriptive statistics

Standard deviation15169.021
Coefficient of variation (CV)5.3166412
Kurtosis478.61708
Mean2853.1211
Median Absolute Deviation (MAD)292.92
Skewness18.572946
Sum1.8614047 × 108
Variance2.3009919 × 108
MonotonicityNot monotonic
2023-01-21T13:36:41.207597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
784.97 115
 
0.2%
817.68 115
 
0.2%
294.72 110
 
0.2%
307 104
 
0.2%
597.14 102
 
0.2%
622.02 101
 
0.2%
824.39 100
 
0.2%
791.41 99
 
0.2%
401.16 95
 
0.1%
204.66 92
 
0.1%
Other values (17856) 64208
98.4%
ValueCountFrequency (%)
200.01 6
< 0.1%
200.06 6
< 0.1%
200.08 1
 
< 0.1%
200.14 3
< 0.1%
200.15 5
< 0.1%
200.19 7
< 0.1%
200.21 1
 
< 0.1%
200.3 3
< 0.1%
200.36 1
 
< 0.1%
200.37 6
< 0.1%
ValueCountFrequency (%)
555376 1
 
< 0.1%
539200 5
< 0.1%
517632 5
< 0.1%
472069.6 2
 
< 0.1%
458320 5
< 0.1%
439987.2 5
< 0.1%
310156.07 1
 
< 0.1%
301122.4 2
 
< 0.1%
297240 1
 
< 0.1%
289077.5 2
 
< 0.1%
Distinct4060
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4710.432
Minimum0
Maximum632610.16
Zeros255
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:41.432431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile391.14
Q1561.04
median999.75
Q32321.4
95-th percentile16425.12
Maximum632610.16
Range632610.16
Interquartile range (IQR)1760.36

Descriptive statistics

Standard deviation20702.61
Coefficient of variation (CV)4.3950554
Kurtosis278.54884
Mean4710.432
Median Absolute Deviation (MAD)526.47
Skewness14.07067
Sum3.0731329 × 108
Variance4.2859804 × 108
MonotonicityNot monotonic
2023-01-21T13:36:41.650501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1431.23 590
 
0.9%
1627.84 530
 
0.8%
803.86 498
 
0.8%
596 448
 
0.7%
1254.1899 418
 
0.6%
966.44 376
 
0.6%
439.7 372
 
0.6%
507.75 363
 
0.6%
767.75 348
 
0.5%
939.57 343
 
0.5%
Other values (4050) 60955
93.4%
ValueCountFrequency (%)
0 255
0.4%
194 2
 
< 0.1%
195.61 1
 
< 0.1%
198.396 1
 
< 0.1%
198.63 1
 
< 0.1%
200.7 8
 
< 0.1%
200.8 1
 
< 0.1%
201.69 3
 
< 0.1%
202.14 1
 
< 0.1%
202.6 1
 
< 0.1%
ValueCountFrequency (%)
632610.16 5
< 0.1%
624453.75 2
 
< 0.1%
539200 11
< 0.1%
458320 12
< 0.1%
391924.7232 5
< 0.1%
387395 8
< 0.1%
348655.5 2
 
< 0.1%
332196.405 2
 
< 0.1%
330708.3792 5
< 0.1%
310273.7392 2
 
< 0.1%

Sales Cost Amount
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5513
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1662.0231
Minimum0
Maximum366576
Zeros308
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:41.892264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile85.36
Q1167.81
median304.58
Q3688.55
95-th percentile4946.11
Maximum366576
Range366576
Interquartile range (IQR)520.74

Descriptive statistics

Standard deviation9559.3969
Coefficient of variation (CV)5.7516633
Kurtosis613.9028
Mean1662.0231
Median Absolute Deviation (MAD)171.17
Skewness21.004615
Sum1.0843205 × 108
Variance91382069
MonotonicityNot monotonic
2023-01-21T13:36:42.101233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
449.69 534
 
0.8%
475.75 457
 
0.7%
0 308
 
0.5%
134.67 305
 
0.5%
162.89 289
 
0.4%
205.72 253
 
0.4%
159.14 242
 
0.4%
16718.08 234
 
0.4%
546.44 231
 
0.4%
344.28 229
 
0.4%
Other values (5503) 62159
95.3%
ValueCountFrequency (%)
0 308
0.5%
12.97 2
 
< 0.1%
19.55 4
 
< 0.1%
20.8 6
 
< 0.1%
26 1
 
< 0.1%
31.19 4
 
< 0.1%
33.97 3
 
< 0.1%
35.48 2
 
< 0.1%
35.54 5
 
< 0.1%
36.03 1
 
< 0.1%
ValueCountFrequency (%)
366576 7
 
< 0.1%
353292.8 4
 
< 0.1%
311589.6 12
 
< 0.1%
185048.85 2
 
< 0.1%
161446.35 5
 
< 0.1%
157412.85 2
 
< 0.1%
153635.03 5
 
< 0.1%
146630.4 4
 
< 0.1%
141265.56 36
0.1%
137736.24 4
 
< 0.1%

Sales Margin Amount
Real number (ℝ)

Distinct21266
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1191.098
Minimum-3932.93
Maximum188800
Zeros3
Zeros (%)< 0.1%
Negative576
Negative (%)0.9%
Memory size509.8 KiB
2023-01-21T13:36:42.289230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-3932.93
5-th percentile61.54
Q1129.89
median246.48
Q3578.22
95-th percentile3825.85
Maximum188800
Range192732.93
Interquartile range (IQR)448.33

Descriptive statistics

Standard deviation5862.5666
Coefficient of variation (CV)4.9219851
Kurtosis324.74032
Mean1191.098
Median Absolute Deviation (MAD)140.23
Skewness15.567055
Sum77708425
Variance34369688
MonotonicityNot monotonic
2023-01-21T13:36:42.556531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
374.7 93
 
0.1%
5317.17 88
 
0.1%
6235.31 87
 
0.1%
341.72 84
 
0.1%
15.32 69
 
0.1%
37.08 67
 
0.1%
52.8 67
 
0.1%
431.88 64
 
0.1%
464.59 64
 
0.1%
24.53 63
 
0.1%
Other values (21256) 64495
98.9%
ValueCountFrequency (%)
-3932.93 1
< 0.1%
-3764.4 2
< 0.1%
-3673.68 2
< 0.1%
-3608.81 1
< 0.1%
-3414.01 2
< 0.1%
-3132.65 2
< 0.1%
-2533.97 2
< 0.1%
-2508.21 2
< 0.1%
-2488.89 1
< 0.1%
-2103.04 2
< 0.1%
ValueCountFrequency (%)
188800 1
 
< 0.1%
185907.2 2
< 0.1%
172624 3
< 0.1%
164339.2 2
< 0.1%
160480 2
< 0.1%
156773.4 1
 
< 0.1%
156521.04 1
 
< 0.1%
151056 3
< 0.1%
148401.6 3
< 0.1%
147487.37 2
< 0.1%

Sales Price
Real number (ℝ)

Distinct14757
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.25028
Minimum0.33734118
Maximum6035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:42.781923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.33734118
5-th percentile22.425556
Q1100.03
median183.28286
Q3448.22
95-th percentile787.12
Maximum6035
Range6034.6627
Interquartile range (IQR)348.19

Descriptive statistics

Standard deviation250.44861
Coefficient of variation (CV)0.88419546
Kurtosis5.8888259
Mean283.25028
Median Absolute Deviation (MAD)116.23352
Skewness1.3311756
Sum18479532
Variance62724.509
MonotonicityNot monotonic
2023-01-21T13:36:42.969061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140.43 191
 
0.3%
817.68 189
 
0.3%
133.41 181
 
0.3%
824.39 138
 
0.2%
783.17 138
 
0.2%
23.47 136
 
0.2%
82.87333333 125
 
0.2%
230.25 120
 
0.2%
221.04 120
 
0.2%
230.98 120
 
0.2%
Other values (14747) 63783
97.8%
ValueCountFrequency (%)
0.337341176 2
 
< 0.1%
0.3514 1
 
< 0.1%
0.361941176 1
 
< 0.1%
0.37718 67
0.1%
0.384 9
 
< 0.1%
0.3888 12
 
< 0.1%
0.3929 67
0.1%
0.3936 5
 
< 0.1%
0.4 9
 
< 0.1%
0.40469 16
 
< 0.1%
ValueCountFrequency (%)
6035 1
< 0.1%
3748 1
< 0.1%
3009.86 1
< 0.1%
3003.41 1
< 0.1%
2823 1
< 0.1%
2753.32 1
< 0.1%
2540.17 1
< 0.1%
2360.1 1
< 0.1%
2314.39 1
< 0.1%
1989.61 1
< 0.1%

Sales Quantity
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct279
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.106712
Minimum1
Maximum16000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:43.133902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q38
95-th percentile86
Maximum16000
Range15999
Interquartile range (IQR)6

Descriptive statistics

Standard deviation429.79373
Coefficient of variation (CV)9.5283764
Kurtosis649.36123
Mean45.106712
Median Absolute Deviation (MAD)2
Skewness23.000674
Sum2942807
Variance184722.65
MonotonicityNot monotonic
2023-01-21T13:36:43.282731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15226
23.3%
2 13466
20.6%
3 7056
10.8%
4 4973
 
7.6%
5 3519
 
5.4%
6 3061
 
4.7%
10 2596
 
4.0%
8 1460
 
2.2%
12 1314
 
2.0%
20 1034
 
1.6%
Other values (269) 11536
17.7%
ValueCountFrequency (%)
1 15226
23.3%
2 13466
20.6%
3 7056
10.8%
4 4973
 
7.6%
5 3519
 
5.4%
6 3061
 
4.7%
7 711
 
1.1%
8 1460
 
2.2%
9 453
 
0.7%
10 2596
 
4.0%
ValueCountFrequency (%)
16000 11
 
< 0.1%
13600 12
 
< 0.1%
9504 7
 
< 0.1%
8316 40
0.1%
7128 21
< 0.1%
7126 2
 
< 0.1%
6480 2
 
< 0.1%
6400 4
 
< 0.1%
5834 4
 
< 0.1%
4752 13
 
< 0.1%

Sales Rep
Real number (ℝ)

Distinct64
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.42142
Minimum103
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size509.8 KiB
2023-01-21T13:36:43.468779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile104
Q1113
median134
Q3160
95-th percentile180
Maximum185
Range82
Interquartile range (IQR)47

Descriptive statistics

Standard deviation26.644271
Coefficient of variation (CV)0.19388732
Kurtosis-1.3014508
Mean137.42142
Median Absolute Deviation (MAD)23
Skewness0.35067578
Sum8965511
Variance709.91716
MonotonicityNot monotonic
2023-01-21T13:36:43.826752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108 6225
 
9.5%
180 4426
 
6.8%
143 2926
 
4.5%
117 2440
 
3.7%
103 2162
 
3.3%
104 2065
 
3.2%
134 2032
 
3.1%
115 1986
 
3.0%
125 1967
 
3.0%
157 1743
 
2.7%
Other values (54) 37269
57.1%
ValueCountFrequency (%)
103 2162
 
3.3%
104 2065
 
3.2%
105 1184
 
1.8%
107 1303
 
2.0%
108 6225
9.5%
109 1137
 
1.7%
110 594
 
0.9%
111 542
 
0.8%
112 486
 
0.7%
113 1417
 
2.2%
ValueCountFrequency (%)
185 537
 
0.8%
184 228
 
0.3%
183 326
 
0.5%
182 808
 
1.2%
181 792
 
1.2%
180 4426
6.8%
179 875
 
1.3%
176 1081
 
1.7%
175 1321
 
2.0%
173 794
 
1.2%

U/M
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size509.8 KiB
EA
58954 
SE
 
5628
PR
 
659

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters130482
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEA
2nd rowEA
3rd rowSE
4th rowEA
5th rowEA

Common Values

ValueCountFrequency (%)
EA 58954
90.4%
SE 5628
 
8.6%
PR 659
 
1.0%

Length

2023-01-21T13:36:44.023270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T13:36:44.142846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ea 58954
90.4%
se 5628
 
8.6%
pr 659
 
1.0%

Most occurring characters

ValueCountFrequency (%)
E 64582
49.5%
A 58954
45.2%
S 5628
 
4.3%
P 659
 
0.5%
R 659
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 130482
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 64582
49.5%
A 58954
45.2%
S 5628
 
4.3%
P 659
 
0.5%
R 659
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 130482
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 64582
49.5%
A 58954
45.2%
S 5628
 
4.3%
P 659
 
0.5%
R 659
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 64582
49.5%
A 58954
45.2%
S 5628
 
4.3%
P 659
 
0.5%
R 659
 
0.5%

Interactions

2023-01-21T13:36:30.179522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:52.872535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:56.164640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:59.137569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:02.286607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:05.345265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:07.954683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:10.501380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:13.227403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:16.193261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:18.766204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:21.632036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:24.999179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:27.617173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:30.348470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:53.544843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:56.346630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:59.395696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:02.544647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:05.572325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:08.117435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:10.673148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:13.638726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:16.324836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:18.982363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:21.803652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:25.131531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:27.763435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:30.544731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:53.773535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:56.569639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:59.650274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:02.772678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:05.738276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:08.257131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:10.894591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:13.829003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:16.550778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:19.158537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:22.045507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:25.364778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:27.897277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:30.668158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:53.982607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:56.720068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:59.892406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:03.029408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:05.925086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:08.433352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:11.048815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:14.031512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:16.708102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:19.412000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:22.436697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:25.619329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:28.125591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:30.787489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:54.120358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:56.982946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:00.068653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:03.160002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:06.163317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:08.561399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:11.229123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:14.226341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:16.972633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:19.559490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:22.616975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:25.830613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:28.269220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:31.179159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:54.294265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:57.264353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:00.251756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:03.319564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:06.327724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:08.732713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:11.434388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:14.455954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:17.140462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:19.768496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:22.923736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:25.968946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:28.418762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:31.359177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:54.550282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:57.525778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:00.500461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:03.589784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:06.530787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:08.915447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:11.608028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:14.637746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:17.311645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:19.928712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:23.250634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:26.139085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:28.548001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:31.515977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:54.710196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:57.699395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:00.734477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:03.744515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:06.720566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:09.075110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:11.796052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:14.799356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:17.524057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:20.170100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:23.450593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:26.288820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:28.710320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:31.673873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:54.939989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:57.857665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:00.932809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:03.981973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:06.885713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:09.290993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:11.969755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:15.017749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:17.719303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:20.382417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:23.675681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:26.436591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:28.873628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:31.877972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:55.081625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:58.072942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:01.188433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:04.211548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:07.102251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:09.511708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:12.182911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:15.178904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:17.875957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:20.648947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:23.934670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:26.669444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:29.108680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:32.153420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:55.311193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:58.327905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:01.351465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:04.417527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:07.271666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:09.793979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:12.342438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:15.397431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:18.044888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:20.829456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:24.214844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:26.808293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:29.382910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:32.346098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:55.537884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:58.514008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:01.542794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:04.595784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:07.429328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:09.955985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:12.530906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:15.573837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:18.211915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:21.062455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:24.489712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:26.947390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:29.594056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:32.520509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:55.668800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:58.680748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:01.773229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:04.765807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:07.599453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:10.134798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:12.730784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:15.846636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:18.386043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:21.241263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:24.668592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:27.106007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:29.855980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:32.714582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:55.997575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:35:58.944590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:02.016905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:04.947017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:07.822883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:10.307814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:12.991138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:16.013231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:18.554413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:21.474447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:24.874494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:27.345501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-21T13:36:30.014608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-21T13:36:44.299819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Unnamed: 0CustKeyDiscount AmountInvoice NumberLine NumberList PriceOrder NumberSales AmountSales Amount Based on List PriceSales Cost AmountSales Margin AmountSales PriceSales QuantitySales RepItem ClassU/M
Unnamed: 01.0000.0090.0160.0100.028-0.0070.2790.0170.0170.0140.027-0.0070.024-0.0010.0090.026
CustKey0.0091.000-0.016-0.1010.007-0.0080.0810.0150.0060.0190.011-0.0040.011-0.1610.0120.053
Discount Amount0.016-0.0161.0000.0780.0010.437-0.0390.8240.8960.7830.7330.3690.379-0.0450.0000.000
Invoice Number0.010-0.1010.0781.0000.0120.014-0.481-0.0020.0310.023-0.027-0.0190.0120.0070.0000.016
Line Number0.0280.0070.0010.0121.000-0.0420.015-0.171-0.113-0.130-0.202-0.075-0.053-0.0940.0000.076
List Price-0.007-0.0080.4370.014-0.0421.0000.0030.3560.3810.3880.2870.980-0.5440.0150.0130.189
Order Number0.2790.081-0.039-0.4810.0150.0031.0000.000-0.016-0.0270.0370.020-0.0150.0100.0000.009
Sales Amount0.0170.0150.824-0.002-0.1710.3560.0001.0000.9680.9320.8900.3580.4840.0110.0000.009
Sales Amount Based on List Price0.0170.0060.8960.031-0.1130.381-0.0160.9681.0000.9130.8600.3470.476-0.0100.0000.012
Sales Cost Amount0.0140.0190.7830.023-0.1300.388-0.0270.9320.9131.0000.7080.3750.4220.0010.0000.011
Sales Margin Amount0.0270.0110.733-0.027-0.2020.2870.0370.8900.8600.7081.0000.3000.4750.0210.0000.012
Sales Price-0.007-0.0040.369-0.019-0.0750.9800.0200.3580.3470.3750.3001.000-0.5670.0370.0000.094
Sales Quantity0.0240.0110.3790.012-0.053-0.544-0.0150.4840.4760.4220.475-0.5671.000-0.0320.0000.007
Sales Rep-0.001-0.161-0.0450.007-0.0940.0150.0100.011-0.0100.0010.0210.037-0.0321.0000.0210.077
Item Class0.0090.0120.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0211.0000.000
U/M0.0260.0530.0000.0160.0760.1890.0090.0090.0120.0110.0120.0940.0070.0770.0001.000

Missing values

2023-01-21T13:36:33.146194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-21T13:36:34.079960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0CustKeyDateKeyDiscount AmountInvoice DateInvoice NumberItem ClassItem NumberItemLine NumberList PriceOrder NumberPromised Delivery DateSales AmountSales Amount Based on List PriceSales Cost AmountSales Margin AmountSales PriceSales QuantitySales RepU/M
011000222014/07/2017368.790014/07/2017100233P0120910Moms Sliced Turkey1000824.960020024514/07/2017456.17824.96000.0456.17456.1700001127EA
121000222017/10/2017109.730017/10/2017116165P0138076Cutting Edge Foot-Long Hot Dogs1000548.660021315716/10/2017438.93548.66000.0438.93438.9300001127EA
241000451627/05/201796627.940027/05/2017103341P0160776High Top Sweet Onion1000408.520020378528/05/201789248.66185876.60000.089248.66196.150901455124SE
361000786603/09/2017371.014003/09/2017100403P0120910Moms Sliced Turkey2000795.314020043603/09/2017424.30795.31400.0424.30424.3000001149EA
471000935618/06/2017608.080018/06/2017105481P0162550Tell Tale Garlic29000575.000020521318/06/2017541.921150.00000.0541.92270.9600002103EA
581000935618/06/2017424.800018/06/2017105481P0160794High Top Walnuts1800051.880020521318/06/2017353.40778.20000.0353.4023.56000015103EA
691000935618/06/201713492.800018/06/2017105481P0136001Big Time Frozen Cheese Pizza9000412.030020521318/06/201711229.0024721.80000.011229.00187.15000060103EA
7101000935618/06/201710481.100018/06/2017105481P0138076Cutting Edge Foot-Long Hot Dogs13000548.660020521318/06/20178722.0019203.10000.08722.00249.20000035103EA
8111000935618/06/2017404.146518/06/2017105481P0161484Super Creamy Peanut Butter3700050.505120521318/06/2017353.43757.57650.0353.4323.56200015103EA
9121000960616/09/20171287.347616/09/2017100445P0117801Better Fancy Canned Sardines30001379.793820047816/09/20171472.242759.58760.01472.24736.1200002118EA
Unnamed: 0CustKeyDateKeyDiscount AmountInvoice DateInvoice NumberItem ClassItem NumberItemLine NumberList PriceOrder NumberPromised Delivery DateSales AmountSales Amount Based on List PriceSales Cost AmountSales Margin AmountSales PriceSales QuantitySales RepU/M
65231652721001763821/03/2018596.3221/03/2018226497P0130500Blue Label Canned Beets100000634.1132089521/03/2018671.901268.22544.11127.79335.9500002180EA
65232652731001763821/03/20184654.7621/03/2018226498P0120910Moms Sliced Turkey2000824.9632090721/03/20185244.769899.523058.482186.28437.06333312180EA
65233652741001763821/03/20181582.7821/03/2018226497P0138060Gorilla Strawberry Yogurt153000187.0132089521/03/20181783.403366.18768.231015.1799.07777818180EA
65234652751001763821/03/20181095.7021/03/2018226497P0138007Gorilla Jack Cheese1500001103.0032089521/03/20181110.302206.00844.55265.75555.1500002180EA
65235652761001763821/03/2018277.6121/03/2018226497P012040Blue Label Fancy Canned Oysters1800014.7632089521/03/2018312.79590.40268.4044.397.81975040180EA
65236652771001763821/03/2018505.7821/03/2018226497P0113447High Top Oranges8000119.5232089521/03/2018569.901075.68239.95329.9563.3222229180EA
65237652781001763821/03/2018410.7521/03/2018226497P0125906Landslide White Sugar38000436.7832089521/03/2018462.81873.56423.5539.26231.4050002180EA
65238652791001763821/03/2018876.1621/03/2018226497P0161856Moms Potato Salad227001232.9232089521/03/2018987.201863.36574.00413.20123.4000008180EA
65239652801001763821/03/201824226.7721/03/2018226498P0117801Better Fancy Canned Sardines10001431.2332090721/03/201827297.5151524.2816188.9011108.61758.26416736180EA
65240652811001763821/03/201824479.2621/03/2018226498P0127550Imagine Popsicles40001084.6132090721/03/201827582.0252061.2814234.2213347.80574.62541748180EA